研究人员开发了新的机器人视觉运动控制模型，专注于高效和预测性协调。CT-VAM是一个受小脑-丘脑启发的模型，采用紧凑的架构进行快速、任务条件化的动作预测，支持云边协同范式。Chameleon通过整合控制索引的前瞻性记忆来解决观察-动作延迟问题，显著提高了在具有挑战性基准上的性能。此外，一个基于扩散的框架通过整合多模态信号来预测人类运动，从而学习预测性的视觉运动协调。 AI
影响 视觉运动控制的进步可能加速机器人自主性和人机交互。
排序理由 多篇研究论文介绍了用于视觉运动控制的新模型和基准。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
arXiv:2606.09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution. Motivate…
Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution. Motivated by this separation, we propose a cerebello-thala…
arXiv:2603.24576v2 Announce Type: replace-cross Abstract: Robots often observe information that determines a future action long before that action is executed. In a shell game, for example, a robot first sees which cup hides the ball, watches the cups move, and only later needs t…
arXiv:2503.23300v2 Announce Type: replace Abstract: Understanding and predicting human visuomotor coordination is crucial for applications in robotics, human-computer interaction, and assistive technologies. This work introduces a forecasting-based task for visuomotor modeling, w…